DHA Service Access and Coverage

Analysis of population coverage and accessibility of Department of Home Affairs offices across South Africa

1 Overview

Birth and death registration may be incomplete due to the inaccessibility of Home Affairs offices where registration occurs. A potential barrier to registration may be the distance needed to travel. We aim to determine the proportion of the population within reasonable distances of Home Affairs offices.

2 Methodology

2.1 Data Sources

• Shape files: District boundaries from Stats SA, standardised using the {NMCleaner} package
• Population estimates: Stats SA mid-year population estimates (2025), at district level
• Office locations: DHA office coordinates from existing dataset (excludes hospital satellite offices)

2.2 Analytical Approach

We explored three approaches to estimate population coverage:

1. Office-to-office distance: Measured median half-distance between nearest office pairs. This underestimated coverage in rural areas.

2. Uniform grid distribution: Created 1km grid and distributed population uniformly within districts. Ignored population clustering.

3. Inverse power model (selected): Assumed population clusters around offices with distance decay.

2.3 Population Density Models

Inverse Power Model: \[ w_i = \frac{1}{(d_i + 1)^{\alpha}} \]

where \(d_i\) is the distance (km) from grid point \(i\) to the nearest office, and \(\alpha = 1.5\) is the decay parameter.

Population Allocation:

Weights are normalised within each district: \[ \hat{w}_i = \frac{w_i}{\sum_{j \in d} w_j} \]

The population at each grid point is: \[ P_i = P_d \cdot \hat{w}_i \]

where \(P_d\) is the total population of district \(d\).

2.4 Limitations

• Population is modelled, not observed at sub-district level
• Distances are straight-line, not road-network or travel-time
• Office capacity and service quality are not accounted for
• Satellite offices (e.g., in hospitals) are not included

3 National Overview Maps

3.1 Population Distribution by District

3.2 Number of DHA Offices by District

3.3 Modelled Population Distribution

4 Coverage Summary

Table 1

District	Total Population	Population within 10 km	% Population within 10 km	Population within 20 km	% Population within 20 km	Median Distance to Nearest DHA Office (km)
Alfred Nzo	935,303	455,654	49	743,056	79	10
Amajuba	610,841	248,305	41	412,243	67	7
Amathole	792,612	384,504	49	624,102	79	14
Bojanala Platinum	1,985,081	973,231	49	1,498,577	75	7
Buffalo City	0	0		0		7
Cape Winelands	1,014,432	393,345	39	660,490	65	22
Capricorn	1,412,657	704,095	50	1,090,472	77	7
Central Karoo	77,157	19,338	25	33,406	43	61
Chris Hani	717,289	284,460	40	477,557	67	19
City of Cape Town	5,030,497	4,099,372	81	4,680,809	93	6
City of Johannesburg	5,900,321	5,628,983	95	5,892,801	100	5
City of Tshwane	4,038,061	2,807,261	70	3,797,729	94	6
Dr Kenneth Kaunda	807,057	306,766	38	534,494	66	26
Dr Ruth Segomotsi Mompati	474,901	126,707	27	226,657	48	37
Ehlanzeni	1,928,692	949,462	49	1,514,775	79	15
Ekurhuleni	4,059,057	3,606,629	89	4,026,309	99	4
Fezile Dabi	536,755	210,022	39	354,982	66	26
Frances Baard	438,829	252,122	57	344,875	79	7
Garden Route	673,192	231,969	34	359,567	53	26
Gert Sibande	1,367,513	499,243	37	865,389	63	24
Harry Gwala	507,708	244,652	48	393,555	78	15
Joe Gqabi	354,931	120,698	34	201,178	57	26
John Taolo Gaetsewe	296,434	60,904	21	105,422	36	59
King Cetshwayo	992,551	584,675	59	878,508	89	11
Lejweleputswa	698,356	237,433	34	415,209	59	22
Mangaung	857,973	368,652	43	569,402	66	7
Mopani	1,266,834	637,249	50	965,505	76	11
Namakwa	129,515	18,623	14	32,228	25	102
Nelson Mandela Bay	1,263,632	913,990	72	1,177,353	93	6
Ngaka Modiri Molema	916,907	356,377	39	585,529	64	11
Nkangala	1,779,928	915,035	51	1,379,990	78	15
O.R. Tambo	1,623,984	901,159	55	1,383,467	85	11
Overberg	329,835	129,159	39	214,445	65	30
Pixley ka Seme	219,155	44,535	20	78,796	36	61
Sarah Baartman	527,418	137,390	26	236,924	45	36
Sedibeng	1,061,185	698,345	66	956,982	90	5
Sekhukhune	1,333,432	623,524	47	1,030,150	77	14
Thabo Mofutsanyana	810,097	281,906	35	474,807	59	21
Ugu	831,709	410,976	49	648,175	78	16
Umzinyathi	607,975	279,573	46	481,574	79	20
Vhembe	1,527,097	814,784	53	1,210,958	79	11
Waterberg	826,172	270,753	33	469,023	57	20
West Coast	502,576	158,658	32	266,241	53	54
West Rand	1,046,310	595,362	57	881,713	84	7
Xhariep	136,652	32,346	24	58,520	43	33
ZF Mgcawu	295,250	65,365	22	109,046	37	50
Zululand	901,275	416,978	46	696,859	77	14
eThekwini	4,374,202	3,566,221	82	4,314,339	99	4
iLembe	742,038	487,512	66	715,374	96	14
uMgungundlovu	1,220,477	558,333	46	974,950	80	18
uMkhanyakude	711,366	369,938	52	571,470	80	20
uThukela	732,104	233,311	32	401,014	55	29
Total	62,225,326	37,715,880	61	51,016,996	82	14

5 Interactive Map

Explore DHA office locations with coverage buffers (10km, 20km, 50km).

6 Key Findings

ImportantSummary Statistics

• 61% of the population lives within 10km of a DHA office
• 82% of the population lives within 20km of a DHA office
• Rural districts show significantly lower coverage

7 Recommendations

1. Request enumeration area data: Census data at smaller geographic units would improve population distribution estimates
2. Include satellite offices: Hospital-based registration points should be mapped
3. Road network analysis: Travel time may be more relevant than straight-line distance